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In a typical application, a classification model is used to combine multiple biomarkers (predictors) to predict a binary outcome such as diseased/nondiseased, responders/non-responders to therapy, etc.
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In many 'real-world' applications, a classification of large data sets, which are often also imbalanced, is difficult due to the small, but usually more interesting classes.
Through the application of a classification algorithm, we now provide the genome-wide evidence that distinct patterns of copy number aberrations exist in metastatic melanomas.
Then, the application of a classification algorithm to the set of fingerprints divides the thousands of pixels or artificial ORNs into a limited number of classes where ORNs carrying the same type of receptor converge.
Legend as application of a classification in a particular area.
In a recent study exploring motor severity in children with CP, in a high- (Australia) and low-resource (Bangladesh) country, the authors concluded that there was limited application of a classification system such as GMFCS-E&R in the low-resource country [ 20].
Applications require a classification system to perform over the duration of their deployment.
The next step is to adapt the energy management depending on the prediction phase, the elapsed time of the application run, and a classification in terms of resources.
Any decision to do so is therefore a compromise between the practical applications of such a classification and the potential loss of precision in risk estimation.
In a future study, a combination of these two methods can be investigated to further improve the accuracy rate in a classification application.
Though the application of SVM as a classification tool for expression data is not new, its utility to analyse the role of genes of the innate immune response pathway in DENV has not been explored.
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